Hyperspectral Imaging as a Rapid Quality Control Method for Herbal Tea Blends

Djokam, Majolie; Sandasi, Maxleene; Chen, Weiyang; Viljoen, Alvaro; Vermaak, Ilze

doi:10.3390/app7030268

Cited by 32 publications

(16 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the relative percentage of each constituent of the blend per tea bag could be accurately predicted. 12 In conclusion, the abovementioned examples clearly demonstrate the applicability of HSI as a reliable quality control technique in the holistic analysis of natural products without prior chemical information. Advances in the technology should include in-process control protocols such as the implementation of HSI camera technology in manufacturing facilities for the online detection of contaminant material in the raw materials prior to product development or the quality control of finished products before release.…”

mentioning

confidence: 69%

Application of hyperspectral imaging in the quality control of medicinal plants and products

et al. 2017

Self Cite

View full text Add to dashboard Cite

mentioning

confidence: 69%

Application of hyperspectral imaging in the quality control of medicinal plants and products

et al. 2017

Self Cite

View full text Add to dashboard Cite

“…Even though the assessment of plant material is prone to errors with regard to moisture, degree of comminution, analysis of extracts or crude material etc., the number of applications in plant quality control increases. There are reports on the differentiation between black cohosh species (Tankeu et al 2018), determination of geographic origin of Chinese Astragalus samples (Zhang and Nie 2010), quality assessment of South African herbal tea blends (Djokam et al 2017;Sandasi et al 2018), quantification of constituents (Mavimbela et al 2018;Pezzei et al 2018), and most recently even prediction on activities of plant extracts (Nikzad-Langerodi et al 2017;Pezzei et al 2018;Schönbichler et al 2014). However, reliability of IR-based results strongly depends on well-developed reference libraries that integrate closely related and potentially adulterating species to ensure that the spectra obtained is accurate to species.…”

Section: Spectroscopic Techniques: Ultra Violet (Uv) Infrared (Ir) mentioning

confidence: 99%

Botanical ingredient identification and quality assessment: strengths and limitations of analytical techniques

Upton¹,

David

Gafner

et al. 2019

Phytochem Rev

View full text Add to dashboard Cite

Interest in botanical medicines is increasing worldwide with current global market values estimated to be greater than US $72 billion. To ensure ingredients used in botanical preparations are appropriately identified and will deliver the intended benefit while minimizing potential for risk, good manufacturing practices (GMPs) are required. GMPs require that scientifically valid analytical techniques be used. This review discusses a variety of techniques of quality assessment taking into account the different challenges between academicians and manufacturers with the basic premise that all techniques are scientifically valid if used appropriately, and conversely, are limited if not used in a scientifically valid manner. The strengths and limitations, applications and inapplicability, of some of these techniques will be discussed, as will differences between traditional and more modern analytical techniques. A strong emphasis is placed on classical botanical and traditional macromorphological assessment techniques that represent the basis of identification and quality assessment upstream of other techniques such as analytical chemistry and genetics. It will be shown that it is the suite of botanical and chemical techniques that provides the greatest confidence for ensuring the identity and quality of botanical medicines.

show abstract

“…Hyperspectral imaging is accepted as a fast, visual, and non-destructive technique in food and agricultural and research. 6,7 The objective of the research is to develop a hyperspectral imaging system to acquire the reflectance data of sugarcane and various types of trash, resulting in identification of sugarcane and trash using multivariate data analysis.…”

Section: Introductionmentioning

confidence: 99%

Development of sugarcane and trash identification system in sugar production using hyperspectral imaging

Aparatana

Saengprachatanarug

Izumikawa

et al. 2020

Journal of Near Infrared Spectroscopy

View full text Add to dashboard Cite

Classification and differentiation of clean sugarcane from trash (green sugarcane leaf, dry sugarcane leaf, stone, and soil) are important for the sugar payment system at a sugar mill. Currently, the methods used to do this are manual and subjective. Therefore, this study is aimed at accurately differentiating clean sugarcane from trash by using hyperspectral imaging with multivariate analyses. Samples containing sugarcane billets and trash mixed in a ratio of 18:38 were analyzed in this study. The reflectance data of the samples were analyzed in the wavelength range of 400–1000 nm via principal component analysis (PCA). The PCA model was capable of identifying all of the clean sugarcane and trash samples. The spectral loadings of the PCA model show that the sugarcane and trash samples are easily identifiable based on the color (visible light) of each class, water absorption (approximately 970 nm), and chlorophyll absorption (approximately 680 nm). Based on the characteristic wavelengths of the PCA loading peaks, over 90% of the sugarcane and trash samples were differentiated using a multiple linear regression model. Sugarcane and trash are classified by using partial least-squares discriminant analysis and support vector machine models. For all wavelengths, the classification rate is 92.9% and 98.2%, respectively. This shows that sugarcane and trash can be accurately classified and differentiated by using hyperspectral imaging and multivariate analyses.

show abstract

Hyperspectral Imaging as a Rapid Quality Control Method for Herbal Tea Blends

Cited by 32 publications

References 27 publications

Application of hyperspectral imaging in the quality control of medicinal plants and products

Application of hyperspectral imaging in the quality control of medicinal plants and products

Botanical ingredient identification and quality assessment: strengths and limitations of analytical techniques

Development of sugarcane and trash identification system in sugar production using hyperspectral imaging

Contact Info

Product

Resources

About